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Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms

Author(s):




Medium: journal article
Language(s): English
Published in: Buildings, , n. 4, v. 12
Page(s): 395
DOI: 10.3390/buildings12040395
Abstract:

The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short_term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10664329
  • Published on:
    09/05/2022
  • Last updated on:
    01/06/2022
 
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